IDNet_Adversarial_CVPR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# All the imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import glob, os\n",
    "import pandas as pd\n",
    "import keras\n",
    "import numpy as np\n",
    "np.random.seed(1337)  # for reproducibility\n",
    "########################\n",
    "from tqdm import tqdm\n",
    "from random import shuffle as shuffle_array\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "########################\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten,  Convolution1D,Convolution2D, MaxPooling2D , AveragePooling2D\n",
    "from keras.utils import np_utils\n",
    "from keras.layers.core import Reshape\n",
    "########################\n",
    "# This is a comment. 123\n",
    "########################\n",
    "import tensorflow as tf\n",
    "from keras.backend import tensorflow_backend as KTF\n",
    "KTF.set_session(tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=.80), \n",
    "                                                 device_count={'CPU':4, 'GPU':2})))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DeepNet-CNN part:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3309200, 9)\n"
     ]
    }
   ],
   "source": [
    "df_data=pd.read_csv('data_padova_50raw.csv')\n",
    "print(df_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a_x</th>\n",
       "      <th>a_y</th>\n",
       "      <th>a_z</th>\n",
       "      <th>a_mag</th>\n",
       "      <th>g_x</th>\n",
       "      <th>g_y</th>\n",
       "      <th>g_z</th>\n",
       "      <th>g_mag</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.009508</td>\n",
       "      <td>7.057844</td>\n",
       "      <td>2.007554</td>\n",
       "      <td>7.337815</td>\n",
       "      <td>-2.589224</td>\n",
       "      <td>0.298963</td>\n",
       "      <td>0.692806</td>\n",
       "      <td>2.696931</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.113291</td>\n",
       "      <td>6.768838</td>\n",
       "      <td>2.079149</td>\n",
       "      <td>7.081869</td>\n",
       "      <td>-2.482977</td>\n",
       "      <td>0.233107</td>\n",
       "      <td>0.771536</td>\n",
       "      <td>2.610513</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.221598</td>\n",
       "      <td>6.563997</td>\n",
       "      <td>2.087110</td>\n",
       "      <td>6.891386</td>\n",
       "      <td>-2.360598</td>\n",
       "      <td>0.167543</td>\n",
       "      <td>0.857614</td>\n",
       "      <td>2.517141</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.313133</td>\n",
       "      <td>6.436601</td>\n",
       "      <td>2.040757</td>\n",
       "      <td>6.759628</td>\n",
       "      <td>-2.220876</td>\n",
       "      <td>0.114398</td>\n",
       "      <td>0.947024</td>\n",
       "      <td>2.417071</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.403327</td>\n",
       "      <td>6.367139</td>\n",
       "      <td>1.971043</td>\n",
       "      <td>6.677435</td>\n",
       "      <td>-2.061907</td>\n",
       "      <td>0.075674</td>\n",
       "      <td>1.036515</td>\n",
       "      <td>2.309015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.523669</td>\n",
       "      <td>6.360655</td>\n",
       "      <td>1.914970</td>\n",
       "      <td>6.663278</td>\n",
       "      <td>-1.882976</td>\n",
       "      <td>0.041914</td>\n",
       "      <td>1.122601</td>\n",
       "      <td>2.192622</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.685100</td>\n",
       "      <td>6.437406</td>\n",
       "      <td>1.898472</td>\n",
       "      <td>6.746389</td>\n",
       "      <td>-1.687617</td>\n",
       "      <td>0.004463</td>\n",
       "      <td>1.199868</td>\n",
       "      <td>2.070690</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.870936</td>\n",
       "      <td>6.606211</td>\n",
       "      <td>1.922702</td>\n",
       "      <td>6.935224</td>\n",
       "      <td>-1.483801</td>\n",
       "      <td>-0.030889</td>\n",
       "      <td>1.262444</td>\n",
       "      <td>1.948431</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.059214</td>\n",
       "      <td>6.853094</td>\n",
       "      <td>1.962107</td>\n",
       "      <td>7.206712</td>\n",
       "      <td>-1.280098</td>\n",
       "      <td>-0.045461</td>\n",
       "      <td>1.308177</td>\n",
       "      <td>1.830859</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.232468</td>\n",
       "      <td>7.150560</td>\n",
       "      <td>1.987650</td>\n",
       "      <td>7.523314</td>\n",
       "      <td>-1.082981</td>\n",
       "      <td>-0.020679</td>\n",
       "      <td>1.339630</td>\n",
       "      <td>1.722755</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        a_x       a_y       a_z     a_mag       g_x       g_y       g_z  \\\n",
       "0  0.009508  7.057844  2.007554  7.337815 -2.589224  0.298963  0.692806   \n",
       "1  0.113291  6.768838  2.079149  7.081869 -2.482977  0.233107  0.771536   \n",
       "2  0.221598  6.563997  2.087110  6.891386 -2.360598  0.167543  0.857614   \n",
       "3  0.313133  6.436601  2.040757  6.759628 -2.220876  0.114398  0.947024   \n",
       "4  0.403327  6.367139  1.971043  6.677435 -2.061907  0.075674  1.036515   \n",
       "5  0.523669  6.360655  1.914970  6.663278 -1.882976  0.041914  1.122601   \n",
       "6  0.685100  6.437406  1.898472  6.746389 -1.687617  0.004463  1.199868   \n",
       "7  0.870936  6.606211  1.922702  6.935224 -1.483801 -0.030889  1.262444   \n",
       "8  1.059214  6.853094  1.962107  7.206712 -1.280098 -0.045461  1.308177   \n",
       "9  1.232468  7.150560  1.987650  7.523314 -1.082981 -0.020679  1.339630   \n",
       "\n",
       "      g_mag  user_id  \n",
       "0  2.696931        1  \n",
       "1  2.610513        1  \n",
       "2  2.517141        1  \n",
       "3  2.417071        1  \n",
       "4  2.309015        1  \n",
       "5  2.192622        1  \n",
       "6  2.070690        1  \n",
       "7  1.948431        1  \n",
       "8  1.830859        1  \n",
       "9  1.722755        1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'a_x', u'a_y', u'a_z', u'a_mag', u'g_x', u'g_y', u'g_z', u'g_mag',\n",
       "       u'user_id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>g_x</th>\n",
       "      <th>g_y</th>\n",
       "      <th>g_z</th>\n",
       "      <th>g_mag</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.589224</td>\n",
       "      <td>0.298963</td>\n",
       "      <td>0.692806</td>\n",
       "      <td>2.696931</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.482977</td>\n",
       "      <td>0.233107</td>\n",
       "      <td>0.771536</td>\n",
       "      <td>2.610513</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.360598</td>\n",
       "      <td>0.167543</td>\n",
       "      <td>0.857614</td>\n",
       "      <td>2.517141</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.220876</td>\n",
       "      <td>0.114398</td>\n",
       "      <td>0.947024</td>\n",
       "      <td>2.417071</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        g_x       g_y       g_z     g_mag  user_id\n",
       "0 -2.589224  0.298963  0.692806  2.696931        1\n",
       "1 -2.482977  0.233107  0.771536  2.610513        1\n",
       "2 -2.360598  0.167543  0.857614  2.517141        1\n",
       "3 -2.220876  0.114398  0.947024  2.417071        1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_g=df_data.ix[:,[ u'g_x', u'g_y', u'g_z', u'g_mag',u'user_id']]\n",
    "df_g.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_users=pd.unique(df_g.ix[(df_g.ix[:,'g_x'].isnull()),'user_id'])\n",
    "good_users=set(np.arange(1,51)).difference(set(bad_users))\n",
    "\n",
    "df_cleaned=df_data.ix[df_data.user_id.isin(good_users),:]\n",
    "df_cleaned.shape\n",
    "len(good_users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_data=df_cleaned.copy()\n",
    "N_samples=df_data.shape[0]/200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11452"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0794282f90>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eMWkhUimluOv8mbT0OHi/sjXY4YStiGm5pMXT1+8e+MQRbA6nm/ePtnLunOwR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f07b40aaad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_data.a_mag[200:400])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11452, 8, 200, 1)\n"
     ]
    }
   ],
   "source": [
    "col_sensors=[u'a_x', u'a_y', u'a_z', u'a_mag', u'g_x', u'g_y', u'g_z', u'g_mag']\n",
    "X_train_test=df_data.ix[:,col_sensors].as_matrix().T.reshape(N_samples,8,200,1)\n",
    "y_train_test_vec=df_data.ix[:,'user_id'].values[0::200]\n",
    "print(X_train_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0794187250>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cxZ9vPZvFlbag32NpVQEmBTtaJI4eKuGGXAC/hx5qyGWI+pLckI9tZJrEMnWx\n1+4KKMPFoMrmmy0ajza/kUAEfQZ+8/ohfvHyAT51Rg1//NKZ1Pob+581t4QNXz0Hs8nElx5oCPgC\n0FrzH68dpKog8EXFREApxZlzS1heUxjS67MtGcwvz2PHJDntQmCEOn5uPLMKs+kcdOJ0B1cT4HJ7\nae0bZm5p6IJelGNBKWJaLXrcMRqkh+5P7UzS0KAI+jQ8vbWVHz+3l6tXzOIn1y3HPGFhZXZJLr+9\n8QzaB0b4yoObA/qQvLCrnS1Nx/n7SxcGtaiYCiyrLmBH60BSNz+KJ0YMfWKb4mCYXZKD1tDSF5yX\n3tTrwKuhPgxBzzApinNiWy3q89ADDw8aqZ0i6CnG5qN9/MPj21k3p5g7P71iykyUVXVF3PnpFbzf\n2Mv3NuycVqycbg8/fX4fC8rzuHZ1dbRMT1iWVxfQPeRM+p7T8WJwxCfo+VnhCLpPkI0UxEBp9Ge4\nzAlD0CG21aLDLg/Do55pZ4lOxBD09ghMdooHsig6CS19Dm59sIGqgix+8/kzpp1FCHD1ilkc7rLz\n85f3k2PJ4F8+ftqkQyjueGYXh7vt/P6v157i7acDp8vCaFgMjviyqsITdF/K4dGewMYLGhyJlKDn\nxq5a1CgqCiaGXpLn7xsvHnpqMOzy8DcPNOB0e7nvprUBXwx/d8l8bj1/Lg++d5RbHtx80sKT2+Pl\nlxsP8PD7zXz1wnlctDi2FZ+JgiyMhsfgiBuL2YTVHHoMvSTXQp7VHLyg99gpzMkMytudjOIYeugn\nGnMFbrPRNz5Z02vFQ5/Avz27m73tg9x/81rmB9HZUCnFd65cTLkti58+v5eLfvYa584vpcKWxeaj\nfextH+Rjy6vGhienI7IwGh4DI25sYXjn4LtO64pzgg65HOmyh5XhYlCaa4nZoqjROjcYDx18qYvi\noacAG/d08Mf3mvjyeXO4cFHwXrRSii+dO4eXv3UB162u4UDnEM/uaMNqNvHrz63iVzesCqgqNJWR\nhdHQGRwZJT/I/P/JqC/NCcpD11qzt32AxZXh10yU5FnpHx6NyQSr3rGQS3DnrLIgi/YkrRYVD92P\n3enmB0/tZHFlPt/+SHhedG1xDv/2ydMjZFlqsby6gA0fttIx4JQ4epAMjrjDip8b1BXn8tLuDjxe\nHZCD0THgpM8xypKq4OsPJmJUi/Y5XFGfj9vnvxMINkxUZcti456OpCwuEg/dzy9e3k9b/wj/9sll\nYcUohemx2gNlAAAdGElEQVQxFka3Sxw9aHweeviCXl+Sw6hHB9x1cXebL0QWEUHPNcr/o78wasTQ\nC7OD99BHRr1htfaIFyLowKGuIX73diPXr63ljNmxmdmZrhgLo5MNzRCmxxdDDz/kUhdkpsueNl8l\n9OKqyIRcIDbVon0OFwXZmUFnlFUlcXGRCDrwi5cPYDWbwg61CDMjC6OhEzkP3Z+L3hvYwujutgFq\ni7Mj8mVieOixKK3vc4wGvSAKjIUCkzHTJe0FfU/bAH/Zdoyb19ePNQ8SoossjIaGL4YevqhW2rLI\nyjRxoGMooP33tA2wJIT+PZNheOixCLn02V0BzRKdyKxCn6CH2sQsnqS9oN/10n7ys8zcct68eJuS\nNkjFaPC4PV4cLk9EPHSTSXHarIKA7pKGXR4au+0RiZ8D/t7kKiapi7324DotGpTnZ2E2qdQUdKXU\n75RSnUqpneO2FSulXlJKHfD/WxRdM6PD1ubjvLS7g1vOm0tBCN/kQmjIwmjwDDmNsv/IXKfLawrY\ndawft2f69MG97QN4dWQWRMGX2lucG5t+Ln2O4HqhG2SYFLMKs4Pud5MIBOKh3w9cMWHbd4CNWusF\nwEb/70nHnS/uozjXws3nzom3KWmFLIwGTyT6uIxnRU0hI6Ne9s8Qdtl0pBeAVXWhddmcjJJca0wW\nRYNtnTue2uJsWvqCq6ZNBGYUdK31G0DvhM2fAB7wP34AuCbCdkWd94/4Rqn97QXzyAuje50QPLIw\nGjwD/j4u4VaKGpwYODL9XdKbB7pYVJEf0ZzxkrzoV4sOuzw43d6QQi4ANYU5NPempoc+GRVa6zb/\n43YgeRp7+7nrpX2U5Vv5wtmz421KWiILo8FxwkOPTMilviSX/Cwz21qm/lIddnn44Egf5y0ojcgx\nDUrzot+gK9QqUYOaomy6h5yMjAbXNz7ehL0oqn2fyCk/lUqpW5RSDUqphq6urnAPFxHeOdTNe4d7\nue3CeWSFMSxACB1jYTRZS6xjTaRDLiaTYnlNwbTrGJuO9ODyeDl/YVlEjmngi6FH10MPtUrUoKbY\nl4uebHH0UAW9QylVBeD/t3OqHbXW92qt12it15SVRfbCCAWtNT9/aT+VtiyuX1cXb3PSlrFWutN4\niMIJTrTOjdzi/fKaQva2DY4tuE7kzQPdWMwm1s2JbLFdSZ4Fh8szNrAjGhh57qHG0GuKfMVXyRZH\nD1XQnwFu8j++CXg6MuZEn7cOdvNBYx+3XSTeeTyRhdHgiLSHDnDZ0grcXs3TW1tPeW7U4+WFXe2c\nOac44p+T0tzoV4savdBDjaHXjgl6innoSqmHgXeBRUqpFqXUl4AfA5cppQ4Al/p/T3i01tz54n5m\nFWTxmbW18TYnrcm2ZLCgPH/aGK5wgkgMt5jIqtpCFlfm8/D7Tac899SWVlr6hrkpCoPMjQZd0awW\nDddDL8+3kpmhaE41D11rfYPWukprnam1rtFa36e17tFaX6K1XqC1vlRrPTELJiF5aXcHW5uPc9vF\n86UBVwKwenYRHzb14fXKwuhMRGK4xUSUUnzuzDp2tg6cFEt3e7z8+tWDLKu2ccmSyA9jGevnEsWF\n0Z4hFyYVfGMuA5NJUZ2EuehpUynqcnv5f8/uYV5ZLp9ZI955IrBmdhGDI272dw7G25SEJxLDLSbj\nmlXV5Fgy+N6TO+gecuJye/nBUzs52uPg7y5eEJX2sSc6LkbPQ+/x56BPNgoyUGqKcpJO0NMmAfsP\n7zbS2OPg9zevJTMN53kmImvqfQXGHzT2sThCvUJSlUgNt5iILSuTX39uFV996EMuvet1rGYTHQNO\nvnrhPC5bGp1sZCPkEs0Yeq/dGXK4xaCmKJuXdndEyKLYkBaC3jPk5O6NB7hgYRkXhTCJSIgOdcU5\nlOVb2dzYyxfOknqA6RiMkocOcPHiCh695Wzue+sIAFcsq+Sq06uiciyAHIuZ7MyMqJb/9wy5KMkN\nr9lefWkuPXYX/cOjFIQYuok1aSHoP395Pw6Xh3/62JJ4myKMQynFmtlFNBzti7cpCU+0PHSDFbWF\n/PKGVVF7/4mU5lvoiqKg99pdLJkV3l3fnFJfm+HGbjsraiPX+iCapHzsYU/bAH/a1MQXzprN/PLw\nG/QLkeWM2UW09A0nZe/pWBKp8XOJQrQXHLuHnJSGGXKZ6xf0I93BDdSOJykt6B6v5jtPbKcox8I3\nLlkQb3OESThzTgngq0oUpuZ4Et32B0JdcQ5NvdFJCRz1eBkYcVMcZsilriQHk4LDIuiJwe/fPsK2\nln7+v4+fFlIbTSH6LJ1lw5Zl5p2DIuhT4fFqeu2ulBrAUluUQ9egk2FX5HulGGX/xuJrqFjNGdQU\n5YiHngg09Tj42Yv7uGRxOVcvj94CjxAeGSbF2fNKePtQd7xNSVj6HC48Xk1pmAKVSBhzTaNRWm+k\nQ5ZEwImbU5rL4a7AJjslAikp6FprvvfkDswmEz/85LKo5NIKkeOceaW09A3THKVb8PHs7xhMunYD\nxri2svzItbCNN7XFPkGPRtgl3CrR8cwpzeVItz1puoKmpKA/tKmJtw52850rF49N8BYSl3Pm+eLo\n70TZS2/stnPdPe9w9a/f4o5ndjE6w7SeRKF70CdQqeShG71SovElblSglkQgRDW3LBeHy0PnYHKM\nS0w5Qd91rJ9//Z/dXLCwjM9JN8WkYH55HmX5Vt6OYhx92OXhK3/cTIZJ8dk1tdz/TiN/ePdo1I4X\nSQwPvTQ/dWLopXkWsjMzaIrCEImeCIdcAA53JUccPaUEfcjp5mt/2kJRTiZ3fWZFWGW/QuxQSnHe\n/FLeOtiNJ0p9Xf6y/Rh72wf52adW8OPrlnPOvBLuee1QVBblIk2X3ztMpUVRpRS1xdlRaX7Va3eR\nYVIRyQqaW5YHwOHu5Iijp4yga6353oYdHO2x88vrV0XkdkuIHRcsKqPX7ora4OiXdncwqyBrrNnU\nNy9dSPeQk4c2Jb6X3j3kxGI2Ra1SNF7UFedELeRSlBNeHxeDKlsWuZYM9rcnR7+hlBH0h99v5plt\nx7j98kWcObck3uYIQXL+gjJMCl7bF/mpViOjHt480MWlSyvGFsjXzSnmnHkl/PebR6J2VxApuoac\nlOVZU25xv9afix7pBUdf2X9k1htMJsXiKht72kTQY8buYwPc8ZddnLeglL+9YF68zRFCoCjXwsra\nQl7bN+Xwq5B560A3I6NeLl1ycrOpG8+eTfvACG/sT4zRiFPRNehMqQVRg9qiHBwuT8QHRvfaXWHn\noI9nSVU+e9qTY/5t0gu6L27+IYXZmfz8syslbp7EXLSonG0t/WOLgJHi5T0d5FnNnDXhzu3ixRWU\n5ll45INTBzwkEt1DqVVUZLC40teKY0tTZMNsRuvcSLGkysbgiDspWukmtaBrrfn+kzto7LFz9/Wr\nUvKiTycuWuyLb7+yN7Je+hv7uzhvQSkW88mXu8Vs4rrVNWzc00nnYOL2kukecqbktb2mvpj8LDMv\n7mqP6Pt2DzkjFnIBn6CDry9UopPUgv7Qpiae3nqMb166kLPnSdw82Tltlo3qwuyIfsBbjw9zrH9k\nykHHn1lbi9ur2fDhqXM1EwGj7L8shVIWDSxmExcvLmfj3s6IrWOMjHoYHHFTbotcEdbiynyUgt0i\n6NHj3UM93PHMLi5YWMZtF82PtzlCBFBK8ZHTKnnjQPeUk+iDpaHRNx1xbf3kgj6vLI919cU8+kFz\nQsZIU7HsfzyXLa2g1+5ic4RaKHcO+MJ15RH8AsyxmJlTkiseeqR4fmc7P35u79iH/NV9ndz6YAOz\nS3L41edWkSFx85ThimWVuNxeXo9Qtsvmo33kWDLG4rWT8dm1tRzptvP+kcQbjZuKRUXjuWBhGZYM\nE8/uaIvI+xmhs0h66OALuyRDpktSCPr2luP85vVDXPjvr/KxX73JF+//gFmF2dx/8zpsUWz6L8Se\nM2YXUZJr4bmdkfmANzT2sbquCPM0YwevOr2K/Cwzj3zQHJFjRpITZf+pKej5WZlcvWIWf3i3kVcj\nkOFklOhH0kMHOK3aRlOvI6QpSy63l19tPMBxR/RG7hkkhaD/4xWLeeq29aysLaI0z8pXLpjHk19d\nP9bgR0gdMkyKK5ZV8vKeDgZHRsN6r8GRUfa2D3DG7KJp98u2ZPDJVdX87/a2hFscPdGYKzUFHeD/\nXnMaiypt/N2ftnDseHiZJJ0Dfg89wufL6Nsfyl3cIx80cedL+yOezTMZSSHoACtrC/nvm9Zw/83r\n+D9XLCbbkhFvk4Qo8akzahgZ9YZ9G76l6ThefWIY9XR8cf0c3F4v97/dGNYxI02rX+AqIxxCSCRy\nLGb+6/Nn4Bj1cP87jWG9V+egE7NJUZQT2TWH5TUFZGdmsClIQR9yurn75QOcNbeYCxeVRdSmyUga\nQRfSh5W1hcwry+Wxhpaw3qehsReTglV1Mwt6fWkuVy6r4sH3jkZsQTYSNHbbKcu3kmtNrbL/idSV\n5HDFskoeeb8Jexjnv2PASVm+NeL1KJkZJtbUF/He4eAayN37xmF67C6+e+WSmFT6iqALCYdSik+v\nqaXhaF9YwwUajvaxpMpGXoBieOsFcxkccXPfm0dCPmakOdrjYE5JbrzNiAlfXF/PwIibDR+G/kXe\nOTgS8XCLwVlzS9jbPjjWbz0QW/77zcN8dHlVzIZMi6ALCcm1q6qxZJi4943DIb1+1ONla/Nx1swQ\nPx/P8ppCPnp6Ffe8fjAqk3RCobHHzuyS9FgrWl1XxLJqG0+EURPQNeiMeIaLwZn+WoZA4+h3v3wA\nl9vLP1y+KCr2TEZYgq6UalRK7VBKbVVKNUTKKEEot2Vxw7paHt/cElJHvj1tAzhcHtZMkX8+Fd/7\n6BIA/u//7I57Xrrd6aZz0El9aXp46Eopzp1fxq5j/YyMhtbWuHPQGTUPfXlNIbmWDF7Z2zHjvgc7\nB3nkg2b+6sy6mP79IuGhX6S1Xqm1XhOB9xKEMb560XxMJsXdGw8E/dqGRl+hSiALouOpLszmG5cs\n5IVdHTz4Xnxb6x7t8X2RpYuHDr601VGPDmlMoMvtpdfuojxKo/osZhNXnV7F/25vw+GaOs7v9Wq+\nu2EHeVYzX79kQVRsmQoJuQgJS4Uti5vX1/P45haeCzLjpeFoL9WF2SGNILz1/Llcsricf/3L7qiP\nxZuOoz2+KTn1aRJDB1hV54s1h1I5aqR4ltuil+L56TW12F0ent0xdXuKhzYd5YPGPv7pY0tjXj8Q\nrqBr4EWl1Gal1C2RMEgQxvOtyxaysraQbz+2LWCvze3xsulwL2uD9M4NTCbFXZ9dyZzSXL54/we8\neSA+7XUb09BDL82zMrskJyRBj1ZR0XjW1hdRX5LDYw2TF6HtaOnnh/+7h/MWlHLd6uqo2TEV4Qr6\nuVrr1cCVwG1KqfMn7qCUukUp1aCUaujqSuy+00LiYTVncM/nV5OflcmnfvMOGz5smTG2/dbBbnrs\nLq48vSrk4xZkZ/LwLWdRX+IT9QffOxrzmHpjt53SPAv5aVYNfUZdER82HQ/6fJ8oKopezr5Sis+u\nrWPTkV5e3n1yLL2tf5hbHmygNM/KLz67Mi4DScISdK11q//fTuBJYN0k+9yrtV6jtV5TVhb9xHoh\n9agqyOYvXz+X5dWFfOvP2/jyHxqmrSh8aksrtixz2IUcpXlWHr3lbM6dX8o/PbWT2/70YcR7tU9H\nY489rcItBqtmF9E95KQ5yAHSYx56FEMuADevr+e0WTa+/fg2GrvtaK3ZdLiHT/z6bQZH3Nx74xlx\nG4EZsqArpXKVUvnGY+ByYGekDBOE8ZTlW/nTl8/k+1ct4e2DPVx21+s8+G7jKV6cw+Xmxd0dfHT5\nLKzm8KuJC3Iyue+mtfzDRxbx8u5OLv7Za/z8pf1jFZzRpLHHTl0ahVsMVvlztrcFOV+2rX8Ys0lF\nPW6dlZnBrz+3GrdHc9Gdr7H+x6/w2Xvfw5ppYsNXz+G0WQVRPf50hFN+VgE86b+tMAN/0lo/HxGr\nBGESzBkmvnz+XK5YVsn3ntzBPz29i9f2dfHTTy0f84j++N5RHC4P16ycFbHjmkyK2y6az+VLK/jp\nC/u4e+MB7t54gNI8K9kWE5kmE3lZZs6dX8r1a+siIsLdQ046BpwsqbRF4H+QXCyoyCPDpNjXPsjV\nKwJ/XXPvMLMKs2PSfXVOaS7PfeM8Ht/cwp62AW67eD5Xr5gV92aBIQu61vowEMTpFoTIUFucwx++\nuI7732nkR8/u5dK7Xufv/OlhP3puL5cuKZ+y/3k4LKjI57c3ruFQ1xBv7O9iT9sAox7NqMdL16CT\n/3rjMH987yi/vXFN2IPKt/u90+U18fP24oXVnMG8slz2tgfXf7ylz0FNUfBZTaFSW5zD31+2MGbH\nC4TUbhAhpCxKKW5eP4dz5pXyvSd38C9/2Q3AitpCfnnDqqjOlp1Xlse8srxTtjf3Orjp9+/zhd+9\nz59vPZuVYZR7b2vuRyk4rTr9BB1gcaUt6EyXlr7hmDTASmQkD11IahZV5vPYrWfzyu0X8ORXz+HR\nW84ixxIfP6W2OIfHv3IOJbkWbv/z1pCrHcHnoc8vywu4D02qsbgqn9bjw/QPB9ZCeWTUQ+egk9qi\n9FtzGI8IupD0mEyKuWV5rKorIiszvm2Vi3Mt/OS65RzqsnPni/tCeg+tNTta+1leE5uGTomIsXaw\nrz2wKUHGInVNcexCLomICLogRJjzF5Zx/dpafv92Iwc7g+8Weax/hO4hFytq0zPcAj4PHQg4jt7S\n5xd08dAFQYg03/7IIrIyM/h/z+4J+rXbm40F0fT10CttWRTmZAY8x9No4BbLRdFERARdEKJAaZ6V\nr188n1f2dvLG/uAqpDcd6cVqNk072DrVUUqxuDKfPW2Be+iZGYqKKFaJJgMi6IIQJf56fT11xTn8\n8H934/Z4A3qN1pqNezs4d35p3NcD4s3SqgL2tg/g8c7cAqC5z0F1YXZUs5uSARF0QYgSVnMG37tq\nMfs7hnj4g8mbOU3kUNcQzb3DXLykPMrWJT7Lqm2MjHoDmlrV0jec9vFzEEEXhKjykdMqOWtuMT99\nfm9AU5A27ukE4OLFIujL/Dn4O4/N3GWztc9BbZpnuIAIuiBEFaUUP71uBVrDNx/ZOmPoZePeTpZW\n2ULq455qzC3NJSvTxM7W6ePo/Y5Ruodc1BWnXyOziYigC0KUqSvJ4YfXLKPhaB//54kdU8aED3cN\n0dDYy6VLK2JsYWJizjCxpMo2Yx/8Pf7UxiVV6buIbJCeZWiCEGOuWVVNU6+Du17aj1drfnTt6acs\nev5y4wGs5gxuPHt2nKxMPJbNKuCpLa14vXrKBU8jE2ZpVfo1MpuIeOiCECP+7pIFfPvyhTy5pZVr\n//Odk6og93cM8vS2Y9x0Tn3Mx5YlMsuqbQw63TRNMyh8T9sAxbkWyqI4qShZEA9dEGLI1y5ewNJZ\nNm7/8zau+uWbfHJVNfPK8rj3jUPkWc3ccv7ceJuYUBi9xbe1HKe+dPIY+d72QZZU5cdlQlCiIR66\nIMSYixdX8MrtF3LDulqe39nOT57fS11xDhv+9hyKcy3xNi+hWFJlIz/LzLuHeiZ93u3xsq99MC37\nxk+GeOiCEAeKci388JrT+dePL6NtYIRKW1ZMBjMkGxkmxdlzS3j7UPekzzf22HG6vSyR+DkgHrog\nxBWTSVEdoyk7ycr6+aU09w7T1HNqHH23v9eLCLoPEXRBEBKa9fN9058m89K3NR/HkmFifvmpA0fS\nERF0QRASmnlleVTYrLx98GRB11rz4u521s8vwWIWKQMRdEEQEhylFOctKOP1/V3Yne6x7bvbBmju\nHeaKZZVxtC6xEEEXBCHh+dyZdQyOuHl8c8vYthd2tmNScOkSqaw1EEEXBCHhWV1XxKq6Qn7/9hG8\nXo3Wmud2trNuTjElUog1hgi6IAhJwZfOnUNjj4O7Nx7g31/Yx4HOIT6xsjreZiUUkocuCEJScMVp\nlXx0eRV3bzwAwA3r6rh+bW2crUosRNAFQUgKzBkmfn3DKi5fWsGRbjtfv3iBlPtPQARdEISkQSkl\nYZZpCCuGrpS6Qim1Tyl1UCn1nUgZJQiCIARPyIKulMoA/gO4ElgK3KCUWhopwwRBEITgCMdDXwcc\n1Fof1lq7gEeAT0TGLEEQBCFYwhH0amD8KPMW/zZBEAQhDkQ9D10pdYtSqkEp1dDV1RXtwwmCIKQt\n4Qh6KzA+CbTGv+0ktNb3aq3XaK3XlJWVhXE4QRAEYTrCEfQPgAVKqTlKKQtwPfBMZMwSBEEQgiXk\nPHSttVsp9TXgBSAD+J3WelfELBMEQRCCQmmtY3cwpbqAoyG+vBSYfA5VfElUuyBxbRO7giNR7YLE\ntS3V7JqttZ4xZh1TQQ8HpVSD1npNvO2YSKLaBYlrm9gVHIlqFySubelql3RbFARBSBFE0AVBEFKE\nZBL0e+NtwBQkql2QuLaJXcGRqHZB4tqWlnYlTQxdEARBmJ5k8tAFQRCEaUgKQU+UNr1KqVql1KtK\nqd1KqV1KqW/4t9+hlGpVSm31/1wVB9salVI7/Mdv8G8rVkq9pJQ64P+3KMY2LRp3TrYqpQaUUt+M\n1/lSSv1OKdWplNo5btuk50j5+KX/mtuulFodY7v+XSm113/sJ5VShf7t9Uqp4XHn7jcxtmvKv51S\n6rv+87VPKfWRGNv16DibGpVSW/3bY3m+ptKH2F1jWuuE/sFXtHQImAtYgG3A0jjZUgWs9j/OB/bj\nax18B/DtOJ+nRqB0wrafAt/xP/4O8JM4/x3bgdnxOl/A+cBqYOdM5wi4CngOUMBZwKYY23U5YPY/\n/sk4u+rH7xeH8zXp387/OdgGWIE5/s9sRqzsmvD8ncA/x+F8TaUPMbvGksFDT5g2vVrrNq31h/7H\ng8AeErvD5CeAB/yPHwCuiaMtlwCHtNahFpaFjdb6DaB3wuapztEngD9oH+8BhUqpqljZpbV+UWvt\n9v/6Hr5eSTFlivM1FZ8AHtFaO7XWR4CD+D67MbVLKaWAzwAPR+PY0zGNPsTsGksGQU/INr1KqXpg\nFbDJv+lr/tum38U6tOFHAy8qpTYrpW7xb6vQWrf5H7cDFXGwy+B6Tv6Qxft8GUx1jhLpuvsiPk/O\nYI5SaotS6nWl1HlxsGeyv12inK/zgA6t9YFx22J+viboQ8yusWQQ9IRDKZUHPAF8U2s9ANwDzANW\nAm34bvlizbla69X4JkjdppQ6f/yT2nePF5eUJuVr3vZx4DH/pkQ4X6cQz3M0FUqp7wNu4CH/pjag\nTmu9CvgW8CellC2GJiXk324cN3Cy4xDz8zWJPowR7WssGQQ9oDa9sUIplYnvj/WQ1noDgNa6Q2vt\n0Vp7gd8SpVvN6dBat/r/7QSe9NvQYdzC+f/tjLVdfq4EPtRad/htjPv5GsdU5yju151S6q+BjwF/\n5RcC/CGNHv/jzfhi1QtjZdM0f7tEOF9m4FrgUWNbrM/XZPpADK+xZBD0hGnT64/P3Qfs0VrfNW77\n+LjXJ4GdE18bZbtylVL5xmN8C2o78Z2nm/y73QQ8HUu7xnGS1xTv8zWBqc7RM8CN/kyEs4D+cbfN\nUUcpdQXwj8DHtdaOcdvLlG+eL0qpucAC4HAM7Zrqb/cMcL1SyqqUmuO36/1Y2eXnUmCv1rrF2BDL\n8zWVPhDLaywWq7/h/uBbDd6P79v1+3G041x8t0vbga3+n6uAB4Ed/u3PAFUxtmsuvgyDbcAu4xwB\nJcBG4ADwMlAch3OWC/QABeO2xeV84ftSaQNG8cUrvzTVOcKXefAf/mtuB7AmxnYdxBdfNa6z3/j3\nvc7/N94KfAhcHWO7pvzbAd/3n699wJWxtMu//X7gKxP2jeX5mkofYnaNSaWoIAhCipAMIRdBEAQh\nAETQBUEQUgQRdEEQhBRBBF0QBCFFEEEXBEFIEUTQBUEQUgQRdEEQhBRBBF0QBCFF+P8B3oFLtoVm\nJY0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f07b4074bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X_train_test[1,3,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.bincount(y_train_test_vec)>100).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import StratifiedKFold\n",
    "# For a 80-20 split\n",
    "skf = StratifiedKFold(n_splits=5,shuffle=True)\n",
    "ind_split=skf.split(np.zeros(N_samples), y_train_test_vec)\n",
    "train_indices=ind_split.next()[0]\n",
    "test_indices=ind_split.next()[1]\n",
    "shuffle_array(train_indices)\n",
    "shuffle_array(test_indices)\n",
    "###############################\n",
    "y_train=y_train_test_vec[train_indices]\n",
    "y_test=y_train_test_vec[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train=X_train_test[train_indices,:,:,:]\n",
    "X_test=X_train_test[test_indices,:,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nb_classes=50\n",
    "# convert class vectors to binary class matrices\n",
    "#Note that we need to subtract 1 from the user_label to get class label\n",
    "# that begin at 0\n",
    "Y_train = np_utils.to_categorical(y_train-1, nb_classes)\n",
    "Y_test = np_utils.to_categorical(y_test-1, nb_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.save('X_train_idnet.npy',X_train)\n",
    "np.save('X_test_idnet.npy',X_test)\n",
    "np.save('Y_train_idnet.npy',Y_train)\n",
    "np.save('Y_test_idnet.npy',Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9149, 8, 200, 1)\n",
      "(2297, 8, 200, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f07940e9c10>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LS1iQGsmv3znq8gmLbDVy96W9fc1RIQHMnRFp2AXU1/bU8M/9tXzvgjksH+f7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\n2c/7d+4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0794143790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X_test[10,3,:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"IDNet.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(None, 8, 200, 20)\n",
      "(None, 8, 200, 40)\n",
      "(None, 2, 66, 40)\n",
      "(None, 40)\n",
      "Train on 8005 samples, validate on 1144 samples\n",
      "Epoch 1/20\n",
      "8005/8005 [==============================] - 2s - loss: 2.5803 - acc: 0.3549 - val_loss: 1.9333 - val_acc: 0.5411\n",
      "Epoch 2/20\n",
      "8005/8005 [==============================] - 1s - loss: 1.6397 - acc: 0.6091 - val_loss: 1.4227 - val_acc: 0.6705\n",
      "Epoch 3/20\n",
      "8005/8005 [==============================] - 1s - loss: 1.2402 - acc: 0.7182 - val_loss: 1.1582 - val_acc: 0.7308\n",
      "Epoch 4/20\n",
      "8005/8005 [==============================] - 1s - loss: 0.9797 - acc: 0.7921 - val_loss: 0.9485 - val_acc: 0.7841\n",
      "Epoch 5/20\n",
      "8005/8005 [==============================] - 1s - loss: 0.7790 - acc: 0.8437 - val_loss: 0.8002 - val_acc: 0.8243\n",
      "Epoch 6/20\n",
      "8005/8005 [==============================] - 1s - loss: 0.6348 - acc: 0.8723 - val_loss: 0.6859 - val_acc: 0.8593\n",
      "Epoch 7/20\n",
      "8005/8005 [==============================] - 1s - loss: 0.5243 - acc: 0.9021 - val_loss: 0.6277 - val_acc: 0.8628\n",
      "Epoch 8/20\n",
      "8005/8005 [==============================] - 1s - loss: 0.4488 - acc: 0.9223 - val_loss: 0.5459 - val_acc: 0.8855\n",
      "Epoch 9/20\n",
      "8005/8005 [==============================] - 1s - loss: 0.3671 - acc: 0.9382 - val_loss: 0.4963 - val_acc: 0.8995\n",
      "Epoch 10/20\n",
      "8005/8005 [==============================] - 1s - loss: 0.3104 - acc: 0.9508 - val_loss: 0.4459 - val_acc: 0.9047\n",
      "Epoch 11/20\n",
      "8005/8005 [==============================] - 1s - loss: 0.2675 - acc: 0.9597 - val_loss: 0.4332 - val_acc: 0.9021\n",
      "Epoch 12/20\n",
      "8005/8005 [==============================] - 1s - loss: 0.2282 - acc: 0.9644 - val_loss: 0.3913 - val_acc: 0.9143\n",
      "Epoch 13/20\n",
      "8005/8005 [==============================] - 1s - loss: 0.1979 - acc: 0.9684 - val_loss: 0.3804 - val_acc: 0.9143\n",
      "Epoch 14/20\n",
      "8005/8005 [==============================] - 1s - loss: 0.1720 - acc: 0.9743 - val_loss: 0.3680 - val_acc: 0.9108\n",
      "Epoch 15/20\n",
      "8005/8005 [==============================] - 1s - loss: 0.1646 - acc: 0.9740 - val_loss: 0.3586 - val_acc: 0.9143\n",
      "Epoch 16/20\n",
      "8005/8005 [==============================] - 1s - loss: 0.1344 - acc: 0.9803 - val_loss: 0.3405 - val_acc: 0.9161\n",
      "Epoch 17/20\n",
      "8005/8005 [==============================] - 1s - loss: 0.1139 - acc: 0.9838 - val_loss: 0.3440 - val_acc: 0.9143\n",
      "Epoch 18/20\n",
      "8005/8005 [==============================] - 1s - loss: 0.1003 - acc: 0.9848 - val_loss: 0.3269 - val_acc: 0.9161\n",
      "Epoch 19/20\n",
      "8005/8005 [==============================] - 1s - loss: 0.0927 - acc: 0.9844 - val_loss: 0.3216 - val_acc: 0.9170\n",
      "Epoch 20/20\n",
      "8005/8005 [==============================] - 1s - loss: 0.0906 - acc: 0.9850 - val_loss: 0.3370 - val_acc: 0.9091\n",
      "2297/2297 [==============================] - 0s     \n",
      "('Test score:', 0.13834996898380839)\n",
      "('Test accuracy:', 0.97083151913955512)\n"
     ]
    }
   ],
   "source": [
    "def IDNet_cnn_keras(weights_path=None, img_rows=8, img_cols=200):\n",
    "    nb_classes = 50\n",
    "    # size of pooling area for max pooling\n",
    "    nb_pool = 3\n",
    "    ###############################################################\n",
    "    ###############################################################\n",
    "    model = Sequential()\n",
    "    model.add(Convolution2D(20,2,10,dim_ordering='tf', border_mode='same',\n",
    "                            input_shape=(img_rows, img_cols,1)))\n",
    "    model.add(Activation('relu'))\n",
    "    print(model.output_shape)\n",
    "    model.add(Convolution2D(40, 4, 10,border_mode='same',dim_ordering='tf'))\n",
    "    model.add(Activation('tanh'))\n",
    "    print(model.output_shape)\n",
    "    model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))\n",
    "    print(model.output_shape)\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(40))\n",
    "    print(model.output_shape)\n",
    "    model.add(Activation('tanh'))\n",
    "#     model.add(Dropout(0.5))\n",
    "    model.add(Dense(nb_classes, activation='softmax'))\n",
    "    return model\n",
    "model = IDNet_cnn_keras()\n",
    "model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train, Y_train, batch_size=128,validation_split=0.125, nb_epoch=20,verbose=1)\n",
    "score = model.evaluate(X_test, Y_test, verbose=1)\n",
    "print('Test score:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2208/2297 [===========================>..] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.97083151937309531"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=model.predict_classes(X_test)\n",
    "(y_pred==y_test-1).sum()/float(len(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f077406c110>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f07941879d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "fig, ax = plt.subplots(figsize=(25,25)) \n",
    "df_results=pd.DataFrame(data=np.column_stack((y_test-1,y_pred)),columns=['True','Predicted'])\n",
    "CT_results=pd.crosstab(df_results.True,df_results.Predicted).apply(lambda r: r/r.sum(), axis=1)\n",
    "sns.heatmap(CT_results, annot=True, fmt=\".2f\", linewidths=.5,ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "convolution2d_1 (Convolution2D)  (None, 8, 200, 20)    420         convolution2d_input_1[0][0]      \n",
      "____________________________________________________________________________________________________\n",
      "activation_1 (Activation)        (None, 8, 200, 20)    0           convolution2d_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_2 (Convolution2D)  (None, 8, 200, 40)    32040       activation_1[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "activation_2 (Activation)        (None, 8, 200, 40)    0           convolution2d_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "maxpooling2d_1 (MaxPooling2D)    (None, 2, 66, 40)     0           activation_2[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)              (None, 5280)          0           maxpooling2d_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "dense_1 (Dense)                  (None, 40)            211240      flatten_1[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "activation_3 (Activation)        (None, 40)            0           dense_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dense_2 (Dense)                  (None, 50)            2050        activation_3[0][0]               \n",
      "====================================================================================================\n",
      "Total params: 245,750\n",
      "Trainable params: 245,750\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensorflow inits .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import backend\n",
    "from keras.utils import np_utils\n",
    "#############################################\n",
    "from cleverhans.utils_tf import model_train, model_eval, batch_eval\n",
    "from cleverhans.attacks import fgsm\n",
    "from cleverhans.utils import cnn_model\n",
    "# Look at the function below\n",
    "#############################################\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defined TensorFlow model graph.\n"
     ]
    }
   ],
   "source": [
    "# 1: Create TF session and set as Keras backend session\n",
    "sess = tf.Session()\n",
    "keras.backend.set_session(sess)\n",
    "# 2:  Define input TF placeholder\n",
    "x = tf.placeholder(tf.float32, shape=(None, 8, 200, 1))\n",
    "y = tf.placeholder(tf.float32, shape=(None, nb_classes))\n",
    "\n",
    "# 3: Define TF model graph\n",
    "# model = cnn_model_custom(img_rows=32, img_cols=32, channels=3)\n",
    "predictions = model(x)\n",
    "print(\"Defined TensorFlow model graph.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cleverhans.utils_tf2 import model_train as model_train_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0\n",
      "\tEpoch took 1.34588003159 seconds\n",
      "Epoch 1\n",
      "\tEpoch took 1.27791309357 seconds\n",
      "Epoch 2\n",
      "\tEpoch took 1.28005290031 seconds\n",
      "Epoch 3\n",
      "\tEpoch took 1.27791404724 seconds\n",
      "Epoch 4\n",
      "\tEpoch took 1.27769112587 seconds\n",
      "Epoch 5\n",
      "\tEpoch took 1.2743499279 seconds\n",
      "Epoch 6\n",
      "\tEpoch took 1.28346300125 seconds\n",
      "Epoch 7\n",
      "\tEpoch took 1.28828811646 seconds\n",
      "Epoch 8\n",
      "\tEpoch took 1.28429579735 seconds\n",
      "Epoch 9\n",
      "\tEpoch took 1.2813410759 seconds\n",
      "Epoch 10\n",
      "\tEpoch took 1.28468394279 seconds\n",
      "Epoch 11\n",
      "\tEpoch took 1.28443002701 seconds\n",
      "Epoch 12\n",
      "\tEpoch took 1.28654313087 seconds\n",
      "Epoch 13\n",
      "\tEpoch took 1.28310799599 seconds\n",
      "Epoch 14\n",
      "\tEpoch took 1.28889203072 seconds\n",
      "Completed model training.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.54462342037550515,\n",
       " 0.67914671009397531,\n",
       " 0.77666521632884133,\n",
       " 0.79886808639824469,\n",
       " 0.8528515450010935,\n",
       " 0.8807139731408411,\n",
       " 0.90639965000700629,\n",
       " 0.90378754726429844,\n",
       " 0.92903787164933072,\n",
       " 0.93339137733631361,\n",
       " 0.94558118992802576,\n",
       " 0.95820635419645461,\n",
       " 0.94558119205583635,\n",
       " 0.95515890099662881,\n",
       " 0.95690030282510075]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def evaluate():\n",
    "    # Evaluate the accuracy of the CIFAR10 model on legitimate test examples\n",
    "    eval_params = {'batch_size': 128}\n",
    "    accuracy = model_eval(sess, x, y, predictions, X_test, Y_test,\n",
    "                          args=eval_params)\n",
    "#     print('Test accuracy on legitimate test examples: ' + str(accuracy))\n",
    "    return accuracy\n",
    "\n",
    "train_params = {'nb_epochs': 15,\n",
    "    'batch_size': 128,\n",
    "    'learning_rate': 0.1\n",
    "}\n",
    "\n",
    "# from cleverhans.utils_tf import model_train\n",
    "accuracy_plain=model_train_acc(sess, x, y, predictions, X_train, Y_train,evaluate=evaluate,verbose=True, args=train_params)\n",
    "accuracy_plain\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f074c39ecd0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6uEodh4iIWlGT5f3mm2+ivLwcy5Ytw+uvv45rNx9TKpXw8fGxWEBqOUEUsfNQ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\nxjQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f07941322d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(accuracy_plain)\n",
    "plt.xlabel('Training epochs')\n",
    "plt.ylabel('Probability of correct classification')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Craft adversarial examples using Fast Gradient Sign Method (FGSM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "cleverhans/attacks.py:439: UserWarning: attacks.fgsm is deprecated and will be removed on 2017-09-27. Instantiate an object from FastGradientMethod.\n",
      "  warnings.warn(\"attacks.fgsm is deprecated and will be removed on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy on true examples: 0.956900302825\n",
      "Test accuracy on adversarial examples: 0.435785806719\n"
     ]
    }
   ],
   "source": [
    "adv_x = fgsm(x, predictions, eps=0.5)\n",
    "eval_params = {'batch_size': 128}\n",
    "X_test_adv, = batch_eval(sess, [x], [adv_x], [X_test], args=eval_params)\n",
    "\n",
    "accuracy = model_eval(sess, x, y, predictions, X_test, Y_test,\n",
    "                      args=eval_params)\n",
    "print('Test accuracy on true examples: ' + str(accuracy))\n",
    "\n",
    "accuracy = model_eval(sess, x, y, predictions, X_test_adv, Y_test,\n",
    "                      args=eval_params)\n",
    "print('Test accuracy on adversarial examples: ' + str(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2297, 8, 200, 1)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_adv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f074c165610>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f074c36ef10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f074c36eed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(X_test[2,:,:,0])\n",
    "plt.matshow(X_test_adv[2,:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy on adversarial examples: 0.956900302825\n",
      "Test accuracy on adversarial examples: 0.698302132231\n",
      "Test accuracy on adversarial examples: 0.503265128273\n",
      "Test accuracy on adversarial examples: 0.383108400603\n",
      "Test accuracy on adversarial examples: 0.308663473201\n",
      "Test accuracy on adversarial examples: 0.249891162256\n",
      "Test accuracy on adversarial examples: 0.205920766191\n",
      "Test accuracy on adversarial examples: 0.166303874347\n",
      "Test accuracy on adversarial examples: 0.136264692909\n",
      "Test accuracy on adversarial examples: 0.111449716896\n",
      "Test accuracy on adversarial examples: 0.0988245535777\n"
     ]
    }
   ],
   "source": [
    "adv_acc_vec=[]\n",
    "eps_vec=np.linspace(0,2,11)\n",
    "for eps_chosen in eps_vec:\n",
    "    adv_x = fgsm(x, predictions, eps=eps_chosen)\n",
    "    eval_params = {'batch_size': 128}\n",
    "    X_test_adv, = batch_eval(sess, [x], [adv_x], [X_test], args=eval_params)\n",
    "\n",
    "    accuracy = model_eval(sess, x, y, predictions, X_test_adv, Y_test,\n",
    "                          args=eval_params)\n",
    "    print('Test accuracy on adversarial examples: ' + str(accuracy))\n",
    "    adv_acc_vec.append(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 2)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IyIR0ubv+9OnTAIC+ffvy0rI9EBmoxNgod8QmFuDb2Et46O4gfUciIiIT0WXJFxQU4O9/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f074cc16250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(eps_vec,adv_acc_vec,'-*')\n",
    "plt.ylabel('Probability of correct classification')\n",
    "plt.xlabel('$\\epsilon$')\n",
    "plt.xlim([0,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy on adversarial examples: 0.0988245535777\n"
     ]
    }
   ],
   "source": [
    "adv_x = fgsm(x, predictions, eps=2)\n",
    "eval_params = {'batch_size': 128}\n",
    "X_test_adv, = batch_eval(sess, [x], [adv_x], [X_test], args=eval_params)\n",
    "\n",
    "accuracy = model_eval(sess, x, y, predictions, X_test_adv, Y_test,\n",
    "                      args=eval_params)\n",
    "print('Test accuracy on adversarial examples: ' + str(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f073e4cf6d0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f074cc24a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jxvGdSyIiIiIiotbH3LgEgAMHDuDOO+/Ee++953vfJYppsbFjmBab+L6ZFhuMabFMi22I\nabH1mBYbH9NiY8cwLTZ2DNNi6zEtth7TYuu1trTYwKt/y5YtWLp0KYYOHYqKigpEIhGnySmIiIiI\niIio5QocuTx48CBuuukmHDx4EBkZGZg2bRquv/76mPHRUa3CwkJ1xjr1IBx7H2WMZX9hzZbmso6l\nR1COKGjrWWaXs/SyWmZ/1GLuuuuuuDFh/Z6vvfZaYIzr926JCYN2eY0ePTowxjIrYqyezoazxcrZ\ndS2zUY4bN84XI0d35OihZaRQG3F86qmn4u5HOz6N/P203lA58nbaaacFxjzwwAO+GNkbahmVlNeV\nzKgA/J9BziQHuGV9rF271hdjGZWUy7TebTmLcFhZKZbRsA0bNnjK2jkozzntHJSzPWozDefk5HjK\n8pyMXosDBw5EUVERANuMvK6z9AbFrFy5MjDGsm/LCKjlt5Izilu4jEBqy7RefXmNWmaUtYwwupz/\n2miFZYQqaJ1Yy1wE/cba/cwyy7FlZms5UhLvOurfvz+Ki4tNM2RrzxpLpsOiRYs8ZTlSqY12ypFL\nS/aBnK0V8P+e2rNGnivajLJyFFKLmT17tqdseabKGG0m8KDnpxajPXtcnrvaNfzyyy97ypZrzeUv\nVVhmKr/77rsDY7R9WUYuLSz3C1kvueiii9S4wG+xoKAACxcuxCWXXIIlS5bEbVgSERERERFR6xR3\n5PLNN99ERUUFpkyZguzsbMyfPx/nnXde3A2Wl5cjPT099AMlIiIiIiKik1fcxuWYMWMQiURQXFyM\n6upqdOjQAUOHDsXSpUtjbpBpsbGXMS02PqbF1mNabOzj0zAtNvYypsXGLgNMi42HabHxY5gW+y+m\nxdZjWmw9psXWa21psYHvXFZXV2PkyJFITU01jVxOnz4dALB8+XJMnz49qTPHuTQmw2r4WLYrb7iW\nGQ/Dmmk1WY1oTW5u7gmv43oOWNazxGiN+CAulR3tN5eVCct5YalERdfp1q0bKisrAQDr168PPGbZ\nQAjrO7bcgOWN3XWWP1kp0BoIcpnsJAFss85GZ+KNkg2Wp59+2reOpD0I5YPY0vjVHvhyZj3twWN5\n6Foe3nL/coZbrVLgMlugpUNt69atvhh5jVjOL60DZtiwYZ6yrChEz8m0tDQcOnRI3Y52TsrKq2U2\nW+34XGaUdb0HuzwLXZ4rlrqD5TNos/S6PB8t92DLdSWXWSrF2vHK+k9YdQeXRrRlZnfLeWHpaNWu\n4egzrEePHigrK1Mr8i6zd2rXkbyOLR1YssEpnyGAvwGqXeeWTmc5+GO5t7s23qJ/HSLWdrTtujQc\nXY9Prqd9F3ImWJf6mKUTTouRjUnL7xsWrUPIcn+Vx7h8+XJ1+4FN9JKSEhw5cgQlJSWYM2dOXa8s\nERERERERUVTgyOW3336Lr7/+GpmZmdizZw8mTpyIDz74QO0lAPjOJRERERERUWuktxAbyMjIQK9e\nvRCJRJCRkYEuXbrgwIEDOPvss9X4/Px8AEyLBZgWGw/TYuPHMC029jKmxdZjWmw9psX6jyfeMTMt\nNj6mxcaOYVpsPabFxt4O02Ljx7TktNjAxuWmTZvw1ltvoU2bNvjrr79QWVmJM888M2g1IiIiIiIi\nakUCG5e9evVCRUUFzjnnHHTs2BEXXXRRzJRYwNtDU1VVZeops8x4pfUmPP/8856yZUa6sGYXla3+\nsEYTNbKnQPsuZK+c68ilZOkZliMj2oxXYY1WS6695JMmTfKU5ayq2na07z1ohEU7tzdt2uQpy5FD\nbd8WDb/j6Pryex87dqxvPZdZLS29cpYYOdOeNrojRw9lDy/gNnvt3r17fTFylEjruQ46l7XeUcss\nepZRSTl7rWU7jz76qC9Gjmh36tTJF5OSkuIpayOpHTt29JSLi4s9Ze0zyL+V7Dpa8dZbb3nKlnPH\nMnooZ3MGgN9++81TjjWrZFpaGvbv36/GWEYitM+5YsUKT9lyL9WehfGe24mQI4Ouv6fLqKSF5dkc\nnYiwIcsoZFB2kuvM7i4zR2tc6gHa9x5rpD5KGwVJ1m8un92Av96p7Uses5YlIO8X2gijHIW03GNc\nsga069VSJ5H3ey1GjjZZztOZM2cGxsjj00aQZYxlZl/t3llYWOgpW0YutRjLCKhLXd7yG2ufK1nk\n8WjnqWXk3jq6Gnh3+uKLL3Dbbbeha9euaN++PbKyskwbJiIiIiIiotYjcEKfRx55BOXl5Vi1ahXK\nysowefJkfPDBBzFb7mVlZejRo0dSDpaIiIiIiIhOToF5Mr/88gv27t2L3NxcVFdXo7S0FKWlpejd\nu7caP2/ePAD//uHunJycpKbFym2fbGmxln27xCQrHUbjkprUHNJiZUxjpsXKmLDTYrt27Yrff/8d\ngH9SBS0t1pKalKy0WJna3phpsfKPcwPhpMUWFBT4ljVlWqwsA8lLi5UxyUyLffvttz3lZKbFyvMg\nVlrs+eefj++//16Naeq02GRhWmzs7TbHtFgL+ds0dVpsdL0LL7wQJSUlLSIt1lK30c4By/mVrLRY\nS/qorBNZ6lVaTEtIi9Wu62SRxyO/P8AtLVZOiBQV2LjMysrCq6++irVr12LOnDn46aefcO6558aM\nb1iR0So1rrQfSv4w2g3FchLICq5rA1RymYUtrBnftItRzupq2Y4m6AYXVgPesm/LZ9iyZYsvRv4W\n/fv3dzqeoIejdnHeeuutcdcBbDPbyZiG5WhFNjMz0xOjzWRnqQTE2xdgm1VPaxReddVVnvL8+fN9\nMdGZb6MOHjzoi7G8uyk/V79+/Xwx8rywzHYnb9JdunTxrdO1a1dPWWuoyYaibLgB/s4CbTtymaUB\nqj1QLbOoyt9Ufu+33367bx15Dlo6M+TnBmwzrcrj0WLGjx/vKf/xxx++mKDzveH/RyukMsa1A0bO\nLq2xvMfkcg+2xMjrwXW7lmeYXDZ16lRfjKVRKLejdS5aKuk9e/b0lF3qCRabN2/2LRs3bpyn7Dq7\nrksjXn4u7T5kIc9T13tBTk5O3b/bt2+vbscyA6lcpt07ZUecpUEql2nPQnmveuqpp3wxs2bN8pSf\neeYZX4z8bcKasXjBggW+mAcffNBTtnSiyutI65iwzPIqzznt95THY5l11tLZY7lvy/u/Nugin6na\nO6ouM8hqDUfL/UzGaL+f9Z4W2Li89tprsX37dtx4443Yt28fli9frv7QRERERERE1HoFvnMZdd99\n92Hs2LG44oor4saVl5cjPT09lIMjIiIiIiKi5sE0N/mhQ4fw448/BjYsAeCJJ54AAKxcuTKh9z4s\n6SWWPyJs2Y7l/UmXPxzNtNjYy062tFjtt2muabFnnXUWfvnlF/N2mjItNvoH56MaMy1WpvQAjZcW\n27lzZ19Mamqqp6ylxcpUoLDSYrUYmVKkvT8ZlL6kpcW6pOo1Zlqsdi5b02IHDhyIoqIiNcY1Ldbl\n/ayw7sGWGHn+t6a02KA/xRBWWqzmZEqLXbt27QlvA3BLi9WukWhabJ8+fbB7927TM1VLQXS5p7SW\ntFhtOy5psXKZa1qsJc35ZE+LtZyTTZkWa/kutDkmAMOfIgGA7du348orr7SEEhERERERUStkSotd\ns2YN2rVrhwkTJjTCIREREREREVFzY37nkoiIiIiIiCgWU1osERERERERUTxsXBIREREREVHC2Lgk\nIiIiIiKihLFxSURERERERAlj45KIiIiIiIgSxsYlERERERERJYyNSyIiIiIiIkoYG5dERERERESU\nsP8DRmS+eAsEJOkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f074cc24850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(X_test[2,:,:,0])\n",
    "plt.matshow(X_test_adv[2,:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Redefine TF model graph\n",
    "model_2 = model\n",
    "predictions_2 = model_2(x)\n",
    "adv_x_2 = fgsm(x, predictions_2, eps=0.3)\n",
    "predictions_2_adv = model_2(adv_x_2)\n",
    "\n",
    "def evaluate_2():\n",
    "    # Evaluate the accuracy of the adversarialy trained CIFAR10 model on\n",
    "    # legitimate test examples\n",
    "    eval_params = {'batch_size': 128}\n",
    "    accuracy = model_eval(sess, x, y, predictions_2, X_test, Y_test,\n",
    "                          args=eval_params)\n",
    "    print('Test accuracy on legitimate test examples: ' + str(accuracy))\n",
    "\n",
    "    # Evaluate the accuracy of the adversarially trained CIFAR10 model on\n",
    "    # adversarial examples\n",
    "    accuracy_adv = model_eval(sess, x, y, predictions_2_adv, X_test,\n",
    "                              Y_test, args=eval_params)\n",
    "    print('Test accuracy on adversarial examples: ' + str(accuracy_adv))\n",
    "\n",
    "# # Perform adversarial training\n",
    "# model_train(sess, x, y, predictions_2, X_train, Y_train,\n",
    "#             predictions_adv=predictions_2_adv, evaluate=evaluate_2,\n",
    "#             args=train_params)\n",
    "\n",
    "# # Evaluate the accuracy of the CIFAR10 model on adversarial examples\n",
    "# accuracy = model_eval(sess, x, y, predictions_2_adv, X_test, Y_test,\n",
    "#                       args=eval_params)\n",
    "# print('Test accuracy on adversarial examples: ' + str(accuracy))\n",
    "\n",
    "# def evaluate():\n",
    "#     # Evaluate the accuracy of the CIFAR10 model on legitimate test examples\n",
    "#     eval_params = {'batch_size': 128}\n",
    "#     accuracy = model_eval(sess, x, y, predictions, X_test, Y_test,\n",
    "#                           args=eval_params)\n",
    "# #     print('Test accuracy on legitimate test examples: ' + str(accuracy))\n",
    "#     return accuracy\n",
    "\n",
    "# train_params = {'nb_epochs': 15,\n",
    "#     'batch_size': 128,\n",
    "#     'learning_rate': 0.1\n",
    "# }\n",
    "\n",
    "# # from cleverhans.utils_tf import model_train\n",
    "# accuracy=model_train_acc(sess, x, y, predictions, X_train, Y_train,evaluate=evaluate,verbose=True, args=train_params)\n",
    "# accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed model training.\n",
      "Test accuracy on legitimate test examples: 0.894645189793\n",
      "Test accuracy on adversarial examples: 0.629081407733\n"
     ]
    }
   ],
   "source": [
    "# Redefine TF model graph\n",
    "model_2 = model\n",
    "predictions_2 = model_2(x)\n",
    "adv_x_2 = fgsm(x, predictions_2, eps=1)\n",
    "predictions_2_adv = model_2(adv_x_2)\n",
    "\n",
    "train_params = {'nb_epochs': 15,\n",
    "    'batch_size': 128,\n",
    "    'learning_rate': 0.1\n",
    "}\n",
    "\n",
    "# Perform adversarial training\n",
    "model_train(sess, x, y, predictions_2, X_train, Y_train, verbose=False,\n",
    "            predictions_adv=predictions_2_adv, evaluate=None,\n",
    "            args=train_params)\n",
    "\n",
    "\n",
    "accuracy = model_eval(sess, x, y, predictions_2, X_test, Y_test,args=eval_params)\n",
    "print('Test accuracy on legitimate test examples: ' + str(accuracy))\n",
    "\n",
    "accuracy = model_eval(sess, x, y, predictions_2_adv, X_test, Y_test,args=eval_params)\n",
    "print('Test accuracy on adversarial examples: ' + str(accuracy))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(None, 8, 200, 20)\n",
      "(None, 8, 200, 40)\n",
      "(None, 2, 66, 40)\n",
      "(None, 40)\n",
      "Completed model training.\n",
      "0.96168915775\n",
      "0.0\n",
      "Test accuracy on adversarial examples (Pre): 0.96168915775\n",
      "Completed model training.\n",
      "Test accuracy on adversarial examples (Post): 0.969960815527\n",
      "(None, 8, 200, 20)\n",
      "(None, 8, 200, 40)\n",
      "(None, 2, 66, 40)\n",
      "(None, 40)\n",
      "Completed model training.\n",
      "0.97300827075\n",
      "0.2\n",
      "Test accuracy on adversarial examples (Pre): 0.698737482688\n",
      "Completed model training.\n",
      "Test accuracy on adversarial examples (Post): 0.846321287236\n",
      "(None, 8, 200, 20)\n",
      "(None, 8, 200, 40)\n",
      "(None, 2, 66, 40)\n",
      "(None, 40)\n",
      "Completed model training.\n",
      "0.962124509219\n",
      "0.4\n",
      "Test accuracy on adversarial examples (Pre): 0.490639963654\n",
      "Completed model training.\n",
      "Test accuracy on adversarial examples (Post): 0.801044839696\n",
      "(None, 8, 200, 20)\n",
      "(None, 8, 200, 40)\n",
      "(None, 2, 66, 40)\n",
      "(None, 40)\n",
      "Completed model training.\n",
      "0.96734871582\n",
      "0.6\n",
      "Test accuracy on adversarial examples (Pre): 0.377448845569\n",
      "Completed model training.\n",
      "Test accuracy on adversarial examples (Post): 0.742707878753\n",
      "(None, 8, 200, 20)\n",
      "(None, 8, 200, 40)\n",
      "(None, 2, 66, 40)\n",
      "(None, 40)\n",
      "Completed model training.\n",
      "0.965171962419\n",
      "0.8\n",
      "Test accuracy on adversarial examples (Pre): 0.351327817337\n",
      "Completed model training.\n",
      "Test accuracy on adversarial examples (Post): 0.696560730507\n",
      "(None, 8, 200, 20)\n",
      "(None, 8, 200, 40)\n",
      "(None, 2, 66, 40)\n",
      "(None, 40)\n",
      "Completed model training.\n",
      "0.973443621208\n",
      "1.0\n",
      "Test accuracy on adversarial examples (Pre): 0.268175881455\n",
      "Completed model training.\n",
      "Test accuracy on adversarial examples (Post): 0.686112319328\n",
      "(None, 8, 200, 20)\n",
      "(None, 8, 200, 40)\n",
      "(None, 2, 66, 40)\n",
      "(None, 40)\n",
      "Completed model training.\n",
      "0.962124508207\n",
      "1.2\n",
      "Test accuracy on adversarial examples (Pre): 0.21549847567\n",
      "Completed model training.\n",
      "Test accuracy on adversarial examples (Post): 0.63561166459\n",
      "(None, 8, 200, 20)\n",
      "(None, 8, 200, 40)\n",
      "(None, 2, 66, 40)\n",
      "(None, 40)\n",
      "Completed model training.\n",
      "0.975185023036\n",
      "1.4\n",
      "Test accuracy on adversarial examples (Pre): 0.11710927255\n",
      "Completed model training.\n",
      "Test accuracy on adversarial examples (Post): 0.613844142538\n",
      "(None, 8, 200, 20)\n",
      "(None, 8, 200, 40)\n",
      "(None, 2, 66, 40)\n",
      "(None, 40)\n",
      "Completed model training.\n",
      "0.956029601911\n",
      "1.6\n",
      "Test accuracy on adversarial examples (Pre): 0.164562472849\n",
      "Completed model training.\n",
      "Test accuracy on adversarial examples (Post): 0.640400519618\n",
      "(None, 8, 200, 20)\n",
      "(None, 8, 200, 40)\n",
      "(None, 2, 66, 40)\n",
      "(None, 40)\n",
      "Completed model training.\n",
      "0.978667827705\n",
      "1.8\n",
      "Test accuracy on adversarial examples (Pre): 0.127993034347\n",
      "Completed model training.\n",
      "Test accuracy on adversarial examples (Post): 0.605572483957\n",
      "(None, 8, 200, 20)\n",
      "(None, 8, 200, 40)\n",
      "(None, 2, 66, 40)\n",
      "(None, 40)\n",
      "Completed model training.\n",
      "0.974314321006\n",
      "2.0\n",
      "Test accuracy on adversarial examples (Pre): 0.0940356983223\n",
      "Completed model training.\n",
      "Test accuracy on adversarial examples (Post): 0.669133652824\n"
     ]
    }
   ],
   "source": [
    "adv_acc_pre=[]\n",
    "adv_acc_post=[]\n",
    "eps_vec=np.linspace(0,2,11)\n",
    "for eps_chosen in eps_vec:\n",
    "    model = IDNet_cnn_keras()\n",
    "    x = tf.placeholder(tf.float32, shape=(None, 8, 200, 1))\n",
    "    y = tf.placeholder(tf.float32, shape=(None, nb_classes))\n",
    "    predictions = model(x)\n",
    "    accuracy_plain=model_train_acc(sess, x, y, predictions, X_train, Y_train,evaluate=evaluate,verbose=False, args=train_params)\n",
    "    print(accuracy_plain[-1])\n",
    "    print(str(eps_chosen))\n",
    "    adv_x = fgsm(x, predictions, eps=eps_chosen)\n",
    "    eval_params = {'batch_size': 128}\n",
    "    X_test_adv, = batch_eval(sess, [x], [adv_x], [X_test], args=eval_params)\n",
    "\n",
    "    accuracy = model_eval(sess, x, y, predictions, X_test_adv, Y_test,\n",
    "                          args=eval_params)\n",
    "    print('Test accuracy on adversarial examples (Pre): ' + str(accuracy))\n",
    "    adv_acc_pre.append(accuracy)\n",
    "    #########################################\n",
    "    model_2 = model\n",
    "    predictions_2 = model_2(x)\n",
    "    adv_x_2 = fgsm(x, predictions_2, eps=eps_chosen)\n",
    "    predictions_2_adv = model_2(adv_x_2)\n",
    "\n",
    "    train_params = {'nb_epochs': 15,\n",
    "        'batch_size': 128,\n",
    "        'learning_rate': 0.1\n",
    "    }\n",
    "\n",
    "    # Perform adversarial training\n",
    "    model_train(sess, x, y, predictions_2, X_train, Y_train, verbose=False,\n",
    "                predictions_adv=predictions_2_adv, evaluate=None,\n",
    "                args=train_params)\n",
    "\n",
    "    accuracy = model_eval(sess, x, y, predictions_2_adv, X_test, Y_test,args=eval_params)\n",
    "    print('Test accuracy on adversarial examples (Post): ' + str(accuracy))\n",
    "    adv_acc_post.append(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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E/onMZE5fOsvmpB23HF9YXMiiqB8Z2+Glqg5dCCFqjEpL7Dk5OVhb/9NFrVKp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f06e2365950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context(('seaborn-white')):\n",
    "    plt.plot(eps_vec[0:-3],adv_acc_pre[0:-3],'-*',eps_vec[0:-3],adv_acc_post[0:-3],'-d')\n",
    "    plt.ylabel('Probability of correct classification')\n",
    "    plt.xlabel('$\\epsilon$')\n",
    "    plt.xlim([0,1.4])\n",
    "    plt.legend(['Without adversarial training','With adversarial training'])\n",
    "    plt.savefig('Pr_eps.eps')\n",
    "    plt.savefig('Pr_eps.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy on true examples: 0.93295602566\n",
      "Test accuracy on adversarial examples: 0.726599911632\n"
     ]
    }
   ],
   "source": [
    "adv_x = fgsm(x, predictions, eps=0.4)\n",
    "eval_params = {'batch_size': 128}\n",
    "X_test_adv, = batch_eval(sess, [x], [adv_x], [X_test], args=eval_params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f06e149e490>]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TZF59Bd8bW8rx48eoT9ElJSHOgogw79pJU1oaTahrRAOx6qprCACu/XvDPn5f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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f06e149e650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rand_ind=84\n",
    "plt.plot(X_test[rand_ind,3,:,0],'-k')\n",
    "plt.plot(X_test_adv[rand_ind,3,:,0],'-r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ACiDaCdFXGw8ZlRUADRHpzBkAtF27Xrjzz3+mtHlz/pKTw6d79vik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\nJhT6IiIiNvEdcWJX/dvcqAIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f06e128d450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context(('seaborn-white')):\n",
    "    plt.plot(X_test[rand_ind,3,:,0],'-k')\n",
    "    plt.plot(X_test_adv[rand_ind,3,:,0],'-r')\n",
    "    plt.ylabel('Acceleration magnitude ($ms^{-2}$)')\n",
    "    plt.xlabel('Time index')\n",
    "    plt.xlim([0,199])\n",
    "    plt.legend(['True Signal','Adversarial signal'])\n",
    "    plt.savefig('a_mag_adv.eps')\n",
    "    plt.savefig('a_mag_adv.pdf')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}